| ORIGINAL ARTICLE |
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| Year : 2020 | Volume
: 10
| Issue : 1 | Page : 1-11 |
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Computationally efficient system matrix calculation techniques in computed tomography iterative reconstruction
Golshan Mahmoudi1, Mohammad Reza Ay1, Arman Rahmim2, Hossein Ghadiri1
1 Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences; Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
2 Department of Radiology and Physics, University of British Columbia; Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, BC, Canada
Correspondence Address:
Dr. Hossein Ghadiri
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran
Iran
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/jmss.JMSS_29_19
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Background: Relative to classical methods in computed tomography, iterative reconstruction techniques enable significantly improved image qualities and/or lowered patient doses. However, the computational speed is a major concern for these iterative techniques. In the present study, we present a method for fast system matrix calculation based on the line integral model (LIM) to speed up the computations without compromising the image quality. In addition, we develop a hybrid line–area integral model (AIM) that highlights the advantages of both LIM and AIMs. Methods: The contributing detectors for a given pixel and a given projection view, and the length of corresponding intersection lines with pixels, are calculated using our proposed algorithm. For the hybrid method, the respective narrow-angle fan beam was modeled by multiple equally spaced lines. The computed system matrix was evaluated in the context of reconstruction using the simultaneous algebraic reconstruction technique (SART) as well as maximum likelihood expectation maximization (MLEM). Results: The proposed LIM offers a considerable reduction in calculation times compared to the standard Siddon algorithm: 2.9 times faster. Differences in root mean square error and peak signal-to-noise ratio were not significant between the proposed LIM and the Siddon algorithm for both SART and MLEM reconstruction methods (P > 0.05). Meanwhile, the proposed hybrid method resulted in significantly improved image qualities relative to LIM and the Siddon algorithm (P < 0.05), though computations were 4.9 times more intensive than the proposed LIM. Conclusion: We have proposed two fast algorithms to calculate the system matrix. The first is based on LIM and was faster than the Siddon algorithm, with matched image quality, whereas the second method is a hybrid LIM–AIM that achieves significantly improved images though with its computational requirements.
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